Return of the whales: The GRANITE 2022 field season comes to a close

Clara Bird, PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

It’s hard to believe that it’s already been four and half months since we started the field season (check out Lisa’s blog for a recap of where we began), but as of this weekend the GRANITE project’s 8th field season has officially ended! As the gray whales wrap up their foraging season and start heading south for the winter, it’s time for us to put our gear into storage, settle into a new academic year, and start processing the data we spent so much time collecting.

The field season can be quite an intense time (40 days equaling over 255 hours on the water!), so we often don’t take a moment to reflect until the end. But this season has been nothing short of remarkable. As you may remember from past blogs, the past couple years (2020-21) have been a bit concerning, with lower whale numbers than previously observed. Since many of us started working on the project during this time, most of us were expecting another similar season. But we were wrong in the best way. From the very first day, we saw more whales than in previous years and we identified whales from our catalog that we hadn’t seen in several years.

Image 1: Collage of photos from our field season.

We identified friends – old and new!

This season we had 224 sightings of 63 individual whales. Of those 63, 51 were whales from our catalog (meaning we have seen them in a previous season). Of these 51 known whales, we only saw 20 of them last year! This observation brings up interesting questions such as, where did most of these whales forage last year? Why did they return to this area this year? And, the classic end of season question, what’s going to happen next year?

We also identified 12 whales that were not in our catalog, making them new to the GEMM lab. Two of our new whales are extra exciting because they are not just new to us but new to the population; we saw two calves this year! We were fortunate enough to observe two mom-calf pairs in July. One pair was of a “new” mom in our catalog and her calf. We nicknamed this calf “Roly-poly” because when we found this mom-calf pair, we recorded some incredible drone footage of “roly-poly” continuously performing body rolls while their mom was feeding nearby (video 1). 

Video 1: “Roly-poly” body rolling while their mom headstands. NOAA/NMFS permit #21678.

The other pair includes a known GEMM lab whale, Luna, and her calf (currently nicknamed “Lunita”). We recently found “Lunita” feeding on their own in early October (Image 2), meaning that they are now independent from its mom (for more on mom-calf behavior check out Celest’s recent blog). We’ll definitely be on the lookout for Roly-Poly and Lunita next year!

Image 2: (left) drone image of Luna and Lunita together in July and (right) drone image of Lunita on their own in October.  NOAA/NMFS permit #21678.

We flew, we scooped, we collected heaps of data!

From our previous blogs you probably know that in addition to photo-ID images, our other two most important forms of data collection are drone flights (for body condition and behavior data) and fecal samples (for hormone analysis). And this season was a success for both! 

We conducted 124 flights over 49 individual whales. The star of these flights was a local favorite Scarlett who we flew over 18 different times. These repeat samples are crucial data for us because we use them to gain insight into how an individual’s body condition changes throughout the season. We also recorded loads of behavior data, collecting footage of different foraging tactics like headstanding, side-swimming, and surfacing feeding on porcelain crab larvae (video 2)!

Video 2: Two whales surface feeding on porcelain crab larvae. NOAA/NMFS permit #21678.

We also collected 61 fecal samples from 26 individual whales (Image 3). The stars of that dataset were Soléand Peak who tied with 7 samples each. These hard-earned samples provide invaluable insight into the physiology and stress levels of these individuals and are a crucial dataset for the project.

Image 3: Photos of fecal sample collection. Left – a very heavy sample, center: Lisa and Enrico after collecting the first fecal sample of the season, right: Clara and Lisa celebrating a good fecal sample collection.

On top of all that amazing data collection we also collected acoustic data with our hydrophones, prey data from net tows, and biologging data from our tagging efforts. Our hydrophones were in the water all summer recording the sounds that the whales are exposed to, and they were successfully recovered just a few weeks ago (Image 4)! We also conducted 69 net tows to sample the prey near where the whales were feeding and identify which prey the whales might be eating (Image 5). Lastly, we had two very successful tagging weeks during which we deployed (and recovered!) a total of 9 tags, which collected over 30 hours of data (Image 6; check out Kate’s blog for more on that).

Image 4 – Photos from hydrophone recovery.
Image 5: Photos from zooplankton sampling.
Image 6: Collage of photos from our two tagging efforts this season.

Final thoughts

All in all, it’s been an incredible season. We’ve seen the return of old friends, collected lots of awesome data, and had some record-breaking days (28 whales in one day!). As we look toward the analysis phase of the year, we’re excited to dig into our eight-year dataset and work to understand what might explain the increase in whales this year.

To end on a personal note, looking through photos to put in this blog was the loveliest trip down memory lane (even though it only ended a few days ago) – I am so honored and proud to be a part of this team. The work we do is hard; we spend long hours on a small boat together and it can be a bit grueling at times. But, when I think back on this season, my first thoughts are not of the times I felt exhausted or grumpy, but of all the joy we felt together. From the incredible whale encounters to the revitalizing snacks to the off-key sing alongs, there is no other team I would rather do this work with, and I so look forward to seeing what next season brings. Stay tuned for more updates from team GRANITE!

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Surprises at Sea

By Rachel Kaplan, PhD student, OSU College of Earth, Ocean and Atmospheric Sciences and Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

By Renee Albertson, Senior Instructor and Research Associate, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Marine Mammal Institute

Going to sea is always full of surprises, and the most recent Northern California Current (NCC) cruise was no different. We had surprises both logistical and scientific, disappointing and delightful. By the end, what stood out clearly is that with a great team of people like the one aboard the R/V Bell M. Shimada, any challenging situation is made the best of, and any exciting moment is only more so.

Our great science party enjoys the Seattle skyline at the end of the September 2022 NCC cruise.

A few days into the cruise, engine trouble caused the Commanding Officer to decide that we needed to cut the trip short, halt instrument deployment operations, and head in to port. Lucky for us, this new plan included 30 hours of transit to Seattle, and long transits are exactly when we collect marine mammal observations. We were able to keep surveying as we moved up the coast and through the Strait of Juan de Fuca into Seattle. There were many surprises here too – we did not find whales in areas where we have previously sighted many, and overall made fewer sightings than is typical.

For example, we expected to see many whales on the Heceta Head Line (south of Newport), whose shallow depth makes the region a rich underwater garden that supports prey and attracts whales. Instead, we saw hardly any whales in this area. Perhaps they simply weren’t present, or perhaps we missed spotting some whales due to the heavy fog, which makes sighting animals that are not near the ship difficult to impossible. This dearth of animals led us to have to interesting conversations with other researchers as we speculated about what might be going on. The scientists on board these NCC cruises collectively research a wide range of oceanographic fields, including ocean chemistry, phytoplankton, zooplankton, fish, seabirds, and marine mammals. Bringing these data together can provide a better understanding of how the ecosystem is changing over time and help contextualize observations in the moment.

Though we often think about how the distributions of prey structure those of foraging whales, we started to wonder whether a lower trophic level could be at play here. Interestingly, in situ phytoplankton analyses showed a type of diatom called Pseudo-nitzchia along much of our cruise track, with the highest concentration off Cape Meares. In stressful conditions, these diatoms sometimes produce the toxin domoic acid, and we wondered whether this could possibly be related to the low whale counts.

Cells of Pseudo-nitzschia, a genus of microalgae that includes several species that make the neurotoxin domoic acid. NOAA photo courtesy of Vera Trainer.

Along the northern Oregon coast and near the Columbia River, the number of whales we observed increased dramatically. The vast majority were humpbacks, some of which were quite active, breaching and tail slapping the surface of the water. On our best day, we turned into the Strait of Juan de Fuca and sighted about 20 whales in quick succession, as well as a sea otter, and both Steller and California sea lions.

Simultaneously as we surveyed for whales, we were able to continue collecting concurrent echosounder data, which reveals the presence of nearby prey like krill and forage fish. Early in the trip, other researchers also collected krill samples that we could bring back to shore and analyze for their caloric content. Even with a shorter time at sea, we felt lucky to be able to fulfill these scientific goals.

Research cruises always center around two things: science and people. Discussing the scientific surprises we observed with other researchers aboard was inspirational, and left us with interesting questions to pursue. Navigating changes to the cruise plan highlighted the importance of the people aboard even more. Everyone worked together to refine our plans with cooperation and positivity, and we all marveled at what a great group it was, often saying, “Good thing we like each other!”

The cruise ended by transiting under the Fremont Bridge into Lake Union.

On the last day of the cruise, we transited into Seattle, moving through the Ballard Locks and into Lake Union. It was an incredible experience to see the city from the water, and an amazing way to cap off the trip. With the next NCC cruise ahead in a few months, we are excited to get back out to sea together soon and tackle whatever surprises come our way.

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Bombs Away! A Summer of Bomb Calorimetry

By Hadley Robinson, undergraduate student, OSU College of Earth, Ocean, and Atmospheric Sciences and School of Language, Culture, and Society

My name is Hadley Robinson and I am a sophomore undergraduate at OSU, double majoring in Environmental Science and Spanish. This summer, I had the privilege of working with Rachel on her PhD research project involving bomb calorimetry, a technique that allows you to quantify the caloric content of organisms like the zooplankton krill.

Hadley preparing the bomb calorimetry machine to run a sample (photo by Rachel Kaplan).

Prior to this internship, I had never worked in a lab before, and as an environmental science major, I had no previous exposure to oceanography. The connection that Rachel made between our labwork and the broader goal of helping decrease whale entanglement events sparked my interest in this project. Our work this summer aimed to process a set of krill samples collected off the coast of Oregon and Washington, so that we could find the number of calories in single krill, and then look at patterns in krill caloric content based on their species, sex, and other characteristics. 

We first identified the krill by species and sex (this was my favorite part of the experiment!). I not only loved looking at them under the microscope, but I also loved how it became a collaborative process. We quickly began getting each other’s opinions on whether or not a krill was Euphausia pacifica, Thysanoessa spinifera, male, female, sexless, gravid (carrying eggs), and much more.

Female Thysanoessa spinifera krill (photo by Abby Tomita).

After identification, we weighed and dried the krill, and finally turned them into small pellets that could fit in an instrument called a bomb calorimeter. These pellets were placed individually into in a “bomb cell” that could then be filled with oxygen and receive a shock from a metal wire. When the machine sent an electric pulse through the wire and combusted the krill pellet, the water surrounding the bomb cell warmed very slightly. The instrument measures this minute temperature change and uses it to calculate the amount of energy in the combusted material. With this information, we were able to quantify how many calories each krill sample contained. Eventually, this data could be used to create a seasonal caloric map of the ocean. Assuming that foraging whales seek out regions with calorically dense prey, such a map could play a crucial role in predicting whale distributions. 

Working with Rachel taught me how dynamic the world of research really is. There were many variables that we had to control and factor into our process, such as the possibility of high-calorie lipids being lost if the samples became too warm during the identification process, the risk of a dried krill becoming rehumidified if it sat out in the open air, and even the tiny amount of krill powder inevitably lost in the pelletization process. This made me realize that we cannot control everything! Grappling with these realities taught me to think quickly, adapt, and most importantly, realize that it is okay to refine the process of research as it is being conducted. 

Intern Abby (left) pressing the krill powder into a pellet and Hadley (right) prepping the bomb (photo by Rachel Kaplan).

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Decisions, decisions: New GEMM Lab publication reveals trade-offs in prey quantity and quality in gray whale foraging

By Lisa Hildebrand, PhD student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Obtaining enough food is crucial for predators to ensure adequate energy gain for maintenance of vital functions and support for energetically costly life history events (e.g., reproduction). Foraging involves decisions at every step of the process, including prey selection, capture, and consumption, all of which should be as efficient as possible. Making poor foraging decisions can have long-term repercussions on reproductive success and population dynamics (Harris et al. 2007, 2008, Grémillet et al. 2008), and for marine predators that rely on prey that is spatially and temporally dynamic and notoriously patchy (Hyrenbach et al. 2000), these decisions can be especially challenging. Prey abundance and density are frequently used as predictors of marine predator distribution, movement, and foraging effort, with predators often selecting highly abundant or dense prey patches (e.g., Goldbogen et al. 2011, Torres et al. 2020). However, there is increased recognition that prey quality is also an important factor to consider when assessing a predator’s ecology and habitat use (Spitz et al. 2012), and marine predators do show a preference for higher quality prey items (e.g., Haug et al. 2002, Cade et al. 2022). Moreover, negative impacts of low-quality prey on the health and breeding success of some marine mammals (Rosen & Trites 2000, Trites & Donnelly 2003) have been documented. Therefore, examining multiple prey metrics, such as prey quantity and quality, in predator ecology studies is critical.

Figure 1. Site map of the Port Orford TOPAZ/JASPER integrated projects. Blue squares represent the location of the 12 sampling stations within the 2 study sites (site boundaries demarcated with black lines). Brown dot represents the cliff-top observation site where theodolite tracking occurred.

Our integrated TOPAZ/JASPER projects in Port Orford do just this! We collect both prey quantity and quality data from a tandem research kayak, while we track Pacific Coast Feeding Group (PCFG) gray whales from shore. The prey and whale sampling overlap spatially (and often temporally within the same day). This kind of concurrent predator-prey sampling at similar scales is often logistically challenging to achieve, yet because PCFG gray whales have an affinity for nearshore, coastal habitats, it is something we have been able to achieve in Port Orford. Since 2016, a field team comprised of graduate, undergraduate, and high school students has collected data during the month of August to investigate gray whale foraging decisions relative to prey. Every day, a kayak team collects GoPro videos (to assess relative prey abundance; AKA: quantity) and zooplankton samples using a tow net (to assess prey community composition; AKA: quality through caloric content of different species) (Figure 1). At the same time, a cliff team surveys for gray whales from shore and tracks them using a theodolite, which provides us with tracklines of individual whales; We categorize each location of a whale into three broad behavior states (feeding, searching, transiting) based on movement patterns. Over the years, the various students who have participated in the TOPAZ/JASPER projects have written many blog posts, which I encourage you to read here (particularly to get more detailed information about the field methods). 

Figure 2. An example daily layer of relative prey abundance (increasing color darkness corresponds with increasing abundance) in one study site with a whale theodolite trackline recorded on the same day overlaid and color-coded by behavioral state.

Several years of data are needed to conduct a robust analysis for our ecological questions about prey choice, but after seven years, we finally had the data and I am excited to share the results, which are due to the many years of hard work from many students! Our recent paper in Marine Ecology Progress Series aimed to determine whether PCFG gray whale foraging decisions are driven by prey quantity (abundance) or quality (caloric content of species) at a scale of 20 m (which is slightly less than 2 adult gray whale body lengths). In this study, we built upon results from my previous Master’s publication, which revealed that there are significant differences in the caloric content between the six common nearshore zooplankton prey species that PCFG gray whales feed on (Hildebrand et al. 2021). Therefore, in this study we addressed the hypothesis that individual whales will select areas where the prey community is dominated by the mysid shrimp Neomysis rayii, since it is significantly higher in caloric content than the other two prey species we identified, Holmesimysis sculpta (a medium quality mysid shrimp species) and Atylus tridens (a low quality amphipod species) (Hildebrand et al. 2021). We used spatial statistics and model to make daily maps of prey abundance and quality that we compared to our whale tracks and behavior from the same day. Please read our paper for the details on our novel methods that produced a dizzying amount of prey layers, which allowed us to tease apart whether gray whales target prey quantity, quality, or a mixture of both when they forage. 

Figure 3. Figure shows the probability of gray whale foraging relative to prey abundance (color-coded by prey species). Dark grey vertical line represents the mean threshold for the H. sculpta curves (12.0); light grey vertical lines: minimum (7.2) and maximum (15.3) thresholds for the H. sculpta curves. Inflection points could not be calculated for the N. rayii curves

So, what did we find? The models proved our hypothesis wrong: foraging probability was significantly correlated with the quantity and quality of the mysid H. sculpta, which has significantly lower calories than N. rayii. This result puzzled us, until we started looking at the overall quantity of these two prey types in the study area and realized that the amount of calorically-rich N. rayii never reached a threshold where it was beneficial for gray whales to forage. But, there was a lot of H. sculpta, which likely made for an energetic gain for the whales despite not being as calorically rich as N. rayii. We determined a threshold of H. sculpta relative abundance that is required to initiate the gray whale foraging behavior, and the abundance of N. rayii never came close to this level (Figure 3). Despite not having the highest quality, H. sculpta did have the highest abundance and showed a significant positive relationship with foraging behavior, unlike the other prey items. Interestingly, whales never selected areas dominated by the low-calorie species A. tridens. These results demonstrate trade-off choices by whales for this abundant, medium-quality prey.

To our knowledge, individual baleen whale foraging decisions relative to available prey quantity and quality have not been addressed previously at this very fine-scale. Interestingly, this trade-off between prey quantity and quality has also been detected in humpback whales foraging in Antarctica at depths deeper than where the densest krill patches occur; while the whales are exploiting less dense krill patches, these krill composed of larger, gravid, higher-quality krill (Cade et al. 2022). While it is unclear how baleen whales differentiate between prey species or reproductive stages, several mechanisms have been suggested, including visual and auditory identification (Torres 2017). We assume here that gray whales, and other baleen whale species, can differentiate between prey species. Thus, our results showcase the importance of knowing the quality (such as caloric content) of prey items available to predators to understand their foraging ecology (Spitz et al. 2012). 

References

Cade DE, Kahane-Rapport SR, Wallis B, Goldbogen JA, Friedlaender AS (2022) Evidence for size-selective pre- dation by Antarctic humpback whales. Front Mar Sci 9:747788

Goldbogen JA, Calambokidis J, Oleson E, Potvin J, Pyenson ND, Schorr G, Shadwick RE (2011) Mechanics, hydrody- namics and energetics of blue whale lunge feeding: effi- ciency dependence on krill density. J Exp Biol 214:131−146

Grémillet D, Pichegru L, Kuntz G, Woakes AG, Wilkinson S, Crawford RJM, Ryan PG (2008) A junk-food hypothesis for gannets feeding on fishery waste. Proc R Soc B 275: 1149−1156

Harris MP, Beare D, Toresen R, Nøttestad L, and others (2007) A major increase in snake pipefish (Entelurus aequoreus) in northern European seas since 2003: poten- tial implications for seabird breeding success. Mar Biol 151:973−983

Harris MP, Newell M, Daunt F, Speakman JR, Wanless S (2008) Snake pipefish Entelurus aequoreus are poor food for seabirds. Ibis 150:413−415

Haug T, Lindstrøm U, Nilssen KT (2002) Variations in minke whale (Balaenoptera acutorostrata) diet and body condi- tion in response to ecosystem changes in the Barents Sea. Sarsia 87:409−422

Hildebrand L, Bernard KS, Torres LG (2021) Do gray whales count calories? Comparing energetic values of gray whale prey across two different feeding grounds in the eastern North Pacific. Front Mar Sci 8:1008

Hyrenbach KD, Forney KA, Dayton PK (2000) Marine pro- tected areas and ocean basin management. Aquat Con- serv 10:437−458

Rosen DAS, Trites AW (2000) Pollock and the decline of Steller sea lions: testing the junk-food hypothesis. Can J Zool 78:1243−1250

Spitz J, Trites AW, Becquet V, Brind’Amour A, Cherel Y, Galois R, Ridoux V (2012) Cost of living dictates what whales, dolphins and porpoises eat: the importance of prey quality on predator foraging strategies. PLOS ONE 7:e50096

Torres LG, Barlow DR, Chandler TE, Burnett JD (2020) Insight into the kinematics of blue whale surface forag- ing through drone observations and prey data. PeerJ 8: e8906

Torres LG (2017) A sense of scale: foraging cetaceans’ use of scale-dependent multimodal sensory systems. Mar Mamm Sci 33:1170−1193

Trites AW, Donnelly CP (2003) The decline of Steller sea lions Eumetopias jubatus in Alaska: a review of the nutri- tional stress hypothesis. Mammal Rev 33:3−28

Putting Fitbits on whales: How tag data allows for estimating calories burned by foraging PCFG gray whales

By: Kate Colson, MSc Student, University of British Columbia, Institute for the Oceans and Fisheries, Marine Mammal Research Unit

Hello! My name is Kate Colson and I am a master’s student at the University of British Columbia, co-supervised by Dr. Andrew Trites of the Marine Mammal Research Unit and Dr. Leigh Torres of the GEMM Lab. As part of my thesis work, I have had the opportunity to spend the summer field season with Leigh and the GEMM Lab team. 

For my master’s I am studying the foraging energetics of Pacific Coast Feeding Group (PCFG) gray whales as part of the much larger Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project. Quantifying the energy expenditure of PCFG gray whales during foraging can help establish a baseline for how disturbance impacts the ability of this unique population to meet their energy needs. Additionally, determining how many calories are burned during different PCFG foraging behaviors might help explain why some gray whales are in better body condition than others.

To understand how much energy different PCFG foraging behaviors cost, I am using data from suction cup tags we have temporarily applied on PCFG gray whales (Figure 1). You can read more about the why the GEMM Lab started using these tags in an earlier blog here. What I want to talk about in this blog is how exactly we can use this tag data to estimate energy expenditure of PCFG gray whales. 

Figure 1. The famous “Scarlett” with a suction cup tag just attached using a carbon fiber pole (seen on far right). This minimally invasive tag has many data sensors, all of which sample at high frequencies, that can allow for an estimation of energy expenditure for different gray whale behaviors. Source: GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678 

The suction cups tags used in this project have many data sensors that are useful for describing the movement of the tagged whale including accelerometers, magnetometers, gyroscopes, and pressure sensors, and all are sampling at high frequencies. For example, the accelerometer is taking 400 measurements per second! The accelerometer, magnetometer, and gyroscope take measurements in 3 dimensions along the X, Y, and Z-axes. The whale’s movement around the X-axis indicates roll (if the whale is swimming on its side), while movement around the Y-axis indicates pitch (if the whales head is oriented towards the surface or the sea floor). Changes in the whale’s movement around the Z-axis indicates if the whale is changing its swimming direction. Together, all of these sensors can describe the dive profile, body orientation, fluking behavior, and fine-scale body movements of the animal down to the second (Figure 2). This allows for the behavior of the tagged whale to be specifically described for the entirety of the tag deployment. 

Figure 2. An example of what the tag sensor data looks like. The top panels show the depth of the animal and can be used to determine the diving behavior of the whale. The middle panels show the body roll of the whale (the X axis) —a roll value close to 0 means the whale is swimming “normally” with no rotation to either side, while a higher roll value means the whale is positioned on its side. The bottom panels show the fluking behavior of the animal: each spike is the whale using its tail to propel itself through the water, with higher spikes indicating a stronger fluke stroke. Source: GEMM Lab, NMFS permit no. 21678

Although these suction cup tags are a great advancement in collecting fine-scale data, they do not have a sensor that actually measures the whale’s metabolism, or rate of calories burned by the whale. Thus, to use this fine-scale tag data as an estimate for energy expenditure, a summary metric must be calculated from the data and used as a proxy. The most common metric found in the literature is Overall Dynamic Body Acceleration (ODBA) and many papers have been published discussing the pros and cons of using ODBA as a proxy for energy expenditure (Brown et al., 2013; Gleiss et al., 2011; Halsey, 2017; Halsey et al., 2011; Wilson et al., 2020). The theory behind ODBA is that because an animal’s metabolic rate is primarily comprised of movement costs, then measuring the acceleration of the body is an effective way of determining energy expenditure. This theory might seem very abstract, but if you have ever worn a Fitbit or similar fitness tracking device to estimate how many calories you’ve burned during a workout, the same principle applies. Those fitness devices use accelerometers and other sensors, to measure the movement of your limbs and produce estimates of energy used. 

So now that we’ve established that the goal of my research is to essentially use these suction cup tags as Fitbits for PCFG gray whales, let’s look at how accelerometry data has been used to detect foraging behavior in large whales so far. Many accelerometry tagging studies have used rorquals as a focal species (see Shadwick et al. (2019) for a review). Well-known rorqual species include humpback, fin, and blue whales. These species forage by using lunges to bulk feed on dense prey patches in the water column. Foraging lunges are indicated by isolated periods of high acceleration that are easily detectable in the tag data (Figure 3; Cade et al., 2016; Izadi et al., 2022). 

Figure 3. Top image: A foraging blue whale performing a surface lunge (Photo credit: GEMM Lab). Note the dense aggregation of krill in the whale’s mouth. Bottom image: The signature acceleration signal for lunge feeding (adapted from Izadi et al., 2022). Each color represents one of the 3D axes of whale movement. The discrete periods of high acceleration represent lunges

However, gray whales feed very differently from rorquals. Gray whales primarily suction feed on the benthos, using their head to dig into the sediment and filter prey out of the mud using their baleen. Yet,  PCFG gray whales often perform many other foraging behaviors such as headstanding and side-swimming (Torres et al., 2018). Additionally, PCFG gray whales tend to feed in water depths that are often shallower than their body length. This shallow depth makes it difficult to isolate signals of foraging in the accelerometry data from random variation in the data and separate the tag data into periods of foraging behaviors (Figure 4).

Figure 4. Top image: A foraging PCFG gray whale rolls on its side to feed on mysid prey. Bottom image: The graph shows the accelerometry data from our suction cup tags that can be used to calculate Overall Dynamic Body Acceleration (ODBA) as a way to estimate energy expenditure. Each color represents a different axis in the 3D motion of the whale. The X-axis is the horizontal axis shows forward and backward movement of the whale, the Y-axis shows the side-to-side movement of the whale, and the Z-axis shows the up-down motion of the whale. Note how there are no clear periods of high acceleration in all 3 axes simultaneously to indicate different foraging behaviors like is apparent during lunges of rorqual whales. However, there is a pattern showing that when acceleration in the Z-axis (blue line) is positive, the X- and Y-axes (red and green lines) are negative. Source: GEMM Lab; NMSF permit no. 21678

But there is still hope! Thanks to the GEMM Lab’s previous work describing the foraging behavior of the PCFG sub-group using drone footage, and the video footage available from the suction cup tags deployed on PCFG gray whales, the body orientation calculated from the tag data can be a useful indication of foraging. Specifically, high body roll is apparent in many foraging behaviors known to be used by the PCFG, and when the tag data indicates that the PCFG gray whale is rolled onto its sides, lots of sediment (and sometimes even swarms of mysid prey) is seen in the tag video footage. Therefore, I am busy isolating these high roll events in the collected tag data to identify specific foraging events. 

My next steps after isolating all the roll events will be to use other variables such as duration of the roll event and body pitch (i.e., if the whales head is angled down), to define different foraging behaviors present in the tag data. Then, I will use the accelerometry data to quantify the energetic cost of performing these behaviors, perhaps using ODBA. Hopefully when I visit the GEMM Lab again next summer, I will be ready to share which foraging behavior leads to PCFG gray whales burning the most calories!

References

Brown, D. D., Kays, R., Wikelski, M., Wilson, R., & Klimley, A. P. (2013). Observing the unwatchable through acceleration logging of animal behavior. Animal Biotelemetry1(1), 1–16. https://doi.org/10.1186/2050-3385-1-20

Cade, D. E., Friedlaender, A. S., Calambokidis, J., & Goldbogen, J. A. (2016). Kinematic diversity in rorqual whale feeding mechanisms. Current Biology26(19), 2617–2624. https://doi.org/10.1016/j.cub.2016.07.037

Duley, P. n.d. Fin whales feeding [photograph]. NOAA Northeast Fisheries Science Center Photo Gallery. https://apps-nefsc.fisheries.noaa.gov/rcb/photogallery/finback-whales.html

Gleiss, A. C., Wilson, R. P., & Shepard, E. L. C. (2011). Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure. Methods in Ecology and Evolution2(1), 23–33. https://doi.org/10.1111/j.2041-210X.2010.00057.x

Halsey, L. G. (2017). Relationships grow with time: A note of caution about energy expenditure-proxy correlations, focussing on accelerometry as an example. Functional Ecology31(6), 1176–1183. https://doi.org/10.1111/1365-2435.12822

Halsey, L. G., Shepard, E. L. C., & Wilson, R. P. (2011). Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comparative Biochemistry and Physiology – A Molecular and Integrative Physiology158(3), 305–314. https://doi.org/10.1016/j.cbpa.2010.09.002

Izadi, S., Aguilar de Soto, N., Constantine, R., & Johnson, M. (2022). Feeding tactics of resident Bryde’s whales in New Zealand. Marine Mammal Science, 1–14. https://doi.org/10.1111/mms.12918

Shadwick, R. E., Potvin, J., & Goldbogen, J. A. (2019). Lunge feeding in rorqual whales. Physiology34, 409–418. https://doi.org/10.1152/physiol.00010.2019

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science5, 1–14. https://doi.org/10.3389/fmars.2018.00319

Wilson, R. P., Börger, L., Holton, M. D., Scantlebury, D. M., Gómez-Laich, A., Quintana, F., Rosell, F., Graf, P. M., Williams, H., Gunner, R., Hopkins, L., Marks, N., Geraldi, N. R., Duarte, C. M., Scott, R., Strano, M. S., Robotka, H., Eizaguirre, C., Fahlman, A., & Shepard, E. L. C. (2020). Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. Journal of Animal Ecology89(1), 161–172. https://doi.org/10.1111/1365-2656.13040

Experiencing a Physical Manifestation of my PhD at Sea in the NCC

Rachel Kaplan, PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

I always have a small crisis before heading into the field, whether for a daytrip or a several-month stint. I’m always dying to go – up until the moment when it is actually time to leave, and I decide I’d rather stay home, keep working on whatever has my current focus, and not break my comfortable little routine.

Preparing to leave on the most recent Northern California Current (NCC) cruise was no different. And just as always, a few days into the cruise, I forgot about the rest of my life and normal routines, and became totally immersed in the world of the ship and the places we went. I learned an exponential amount while away. Being physically in the ecosystem that I’m studying immediately had me asking more, and better, questions to explore at sea and also bring back to land. 

Many of these questions and realizations centered on predator-prey relationships between krill and whales at fine spatial scales. We know that distributions of prey species are a big factor in structuring whale distributions in the ocean, and one of our goals on this cruise was to observe these relationships more closely. The cruise offered an incredible opportunity to experience these relationships in real time: while my labmates Dawn and Clara were up on the flying bridge looking for whales, I was down in the acoustics lab, watching incoming echosounder data in order to identify krill aggregations. 

From left, Clara and Dawn survey for marine mammals on the flying bridge.

We used radios to stay in touch with what we were each seeing in real time, and learned quickly that we tended to spot whales and krill almost simultaneously. Experiencing this coherence between predator and prey distributions felt like a physical manifestation of my PhD. It also affirmed my faith in one of our most basic modeling assumptions: that the backscatter signals captured in our active acoustic data are representative of the preyscape that nearby whales are experiencing.

Being at sea with my labmates also catalyzed an incredible synthesis of our different types of knowledge. Because of the way that I think about whale distributions, I usually just focus on whether a certain type of whale is present or not while surveying. But Clara, with her focus on cetacean behavior, thinks in a completely different way from me. She timed the length of dives and commented on the specific behaviors she noticed, bringing a new level of context to our observations. Dawn, who has been joining these cruises for five years now, shared her depth of knowledge built through returning to these places again and again, helping us understand how the system varies through time.

Observing whale behavior, such as for these humpbacks, provides valuable information on how they are using a given area.

One of the best experiences of the cruise for me was when we conducted a targeted net tow in an area of foraging humpbacks on the Heceta Head Line off the central Oregon coast. The combination of the krill signature I was seeing on the acoustics display, and the radio reports from Dawn and Clara of foraging dives, convinced me that this was an opportunity for a net tow,  if possible, to see exactly what zooplankton was in the water near the whales. Our chief scientist, Jennifer Fisher, and the ship’s officers worked together to quickly turn the ship around and get a net in the water, in an effort to catch krill from the aggregation I had seen.  

This unique opportunity gave me a chance to test my own interpretation of the acoustics data, and compare what we captured in the net with what I expected from the backscatter signal. It also prompted me to think more about the synchrony and differences between what is captured by net tows and echosounder data, two primary ways for looking at whale prey. 

Collecting tiny yet precious krill samples associated with foraging humpbacks!

Throughout the entire cruise, the opportunity to build my intuition and notice ecological patterns was invaluable. Ecosystem modeling gives us the opportunity to untangle incredible complexity and put dynamic relationships in mathematical terms, but being out on the ocean provides the chance to develop a feel for these relationships. I’m so glad to bring this new perspective to my next round of models, and excited to continue trying to tease apart fine-scale dynamics between whales and krill.

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Yonder Whales and Nearby Prey: A New Look at a Familiar System

Rachel Kaplan1, Dawn Barlow2, Clara Bird3

1PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

2Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

3PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

What do peanut butter m&ms, killer whales, affogatos, tired eyes, and puffins all have in common? They were all major features of the recent Northern California Current (NCC) ecosystem survey cruise. 

The science party of the May 2022 Northern California Current ecosystem cruise.

We spent May 6–17 aboard the NOAA vessel Bell M. Shimada in northern California, Oregon, and Washington waters. This fabulously interdisciplinary cruise studies multiple aspects of the NCC ecosystem three times per year, and the GEMM lab has put marine mammal observers aboard since 2018.

This cruise was a bit different than usual for the GEMM lab: we had eyes on both the whales and their prey. While Dawn Barlow and Clara Bird observed from sunrise to sunset to sight and identify whales, Rachel Kaplan collected krill data via an echosounder and samples from net tows in order to learn about the preyscape the whales were experiencing. 

From left, Rachel, Dawn, and Clara after enjoying some beautiful sunset sightings. 

We sailed out of Richmond, California and went north, sampling as far north as La Push, Washington and up to 200 miles offshore. Despite several days of challenging conditions due to wind, rain, fog, and swell, the team conducted a successful marine mammal survey. When poor weather prevented work, we turned to our favorite hobbies of coding and snacking.

Rachel attends “Clara’s Beanbag Coding Academy”.

Cruise highlights included several fin whales, sperm whales, killer whales, foraging gray whales, fluke slapping and breaching humpbacks, and a visit by 60 pacific white-sided dolphins. While being stopped at an oceanographic sampling station typically means that we take a break from observing, having more time to watch the whales around us turned out to be quite fortunate on this cruise. We were able to identify two unidentified whales as sei whales after watching them swim near us while paused on station. 

Marine mammal observation segments (black lines) and the sighting locations of marine mammal species observed during the cruise.

On one of our first survey days we also observed humpbacks surface lunge feeding close to the ship, which provided a valuable opportunity for our team to think about how to best collect concurrent prey and whale data. The opportunity to hone in on this predator-prey relationship presented itself in a new way when Dawn and Clara observed many apparently foraging humpbacks on the edge of Heceta Bank. At the same time, Rachel started observing concurrent prey aggregations on the echosounder. After a quick conversation with the chief scientist and the officers on the bridge, the ship turned around so that we could conduct a net tow in order to get a closer look at what exactly the whales were eating.

Success! Rachel collects krill samples collected in an area of foraging humpback whales.

This cruise captured an interesting moment in time: southerly winds were surprisingly common for this time of year, and the composition of the phytoplankton and zooplankton communities indicated that the seasonal process of upwelling had not yet been initiated. Upwelling brings deep, cold, nutrient-rich waters to the surface, generating a jolt of productivity that brings the ecosystem from winter into spring. It was fascinating to talk to all the other researchers on the ship about what they were seeing, and learn about the ways in which it was different from what they expected to see in May.

Experiencing these different conditions in the Northern California Current has given us a new perspective on an ecosystem that we’ve been observing and studying for years. We’re looking forward to digging into the data and seeing how it can help us understand this ecosystem more deeply, especially during a period of continued climate change.

The total number of each marine mammal species observed during the cruise.

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Dive into Oregon’s underwater forests

By Lisa Hildebrand, PhD student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

When I was younger, I aspired to be a marine mammal biologist. I thought it was purely about knowing as much about marine mammal species as possible. However, over time and with experience in this field, I have realized that in order to understand a species, you need to have a holistic understanding of its prey, habitat, and environment. When I first applied to be advised by Leigh in the GEMM Lab, I had no idea how much of my time I would spend looking at tiny zooplankton under a microscope, thinking about the different benefits of different habitat types, or reading about oceanographic processes. But these things have been incredibly vital to my research to date and as a result, I now refer to myself as a marine ecologist. This holistic understanding that I am gaining will only grow throughout my PhD as I am broadly looking at the habitat use, site fidelity, and population dynamics of the Pacific Coast Feeding Group (PCFG) of gray whales for my thesis research. 

The PCFG display many foraging tactics and occupy several habitat types along the Oregon coast while they spend their summer feeding seasons here (Torres et al. 2018). Here, I will focus on one of these habitats: kelp. When you hear the word kelp, you probably conjure an image of long, thick stalks that reach from the ocean floor to the surface, with billowing fronds waving around (Figure 1a). However, this type is only one of three basic morphologies (Filbee-Dexter & Scheibling 2014) and it is called canopy kelp, which often forms extensive forests. The other two morphologies are stipitate and prostrate kelps. The former forms midwater stands (Figure 1b) while the latter forms low-lying kelp beds (Figure 1c). All three of these morphologies exist on the Oregon coast and create a mosaic of understory and canopy kelp patches that dot our coastline.

Figure 1. Examples of the three different kelp morphologies. a: bull kelp (Nereocystis luetkeana) is a type of canopy kelp and the dominant kelp on the Oregon coast (Source: Oregon Coast Aquarium); b: sea palm (Postelsia palmaeformis) is a type of stipitate kelp that forms mid-water stands (Source: Oregon Conservation Strategy); c: sea cabbage (Saccharina sessilis) is a type of prostrate kelp that is stipeless and forms low-lying kelp beds (Source: Central Coast Biodiversity).

One of the most magnificent things about kelp is that it is not just a species itself, but it provides critical habitat, refuge, and food resources to a myriad of other species due to its high rates of primary production (Dayton 1985). Kelp is often referred to as a foundation species due to all of these critical services it provides. In Oregon, many species of rockfish, which are important commercial and recreational fisheries, use kelp as habitat throughout their life cycle, including as nursery grounds. Lingcod, another widely fished species, forages amongst kelp. A large number of macroinvertebrates can be found in Oregon kelp forests, including anemones, limpets, snails, sea urchins, sea stars, and abalone, to name a fraction of them. 

Kelps grow best in cold, nutrient-rich waters (Tegner et al. 1996) and their growth and distribution patterns are highly naturally variable on both temporal and spatial scales (Krumhansl et al. 2016). However, warm water, low nutrient or light conditions, intensive grazing by herbivores, and severe storm activity can lead to the erosion and defoliation of kelp beds (Krumhansl et al. 2016). While these events can occur naturally in cyclical patterns, the frequency of several of these events has increased in recent years, as a result of climate change and anthropogenic impacts. For example, Dawn’s blog discussed increasing marine heatwaves that represent an influx of warm water for a prolonged period of time. In fact, kelps can be useful sentinels of change as they tend to be highly responsive to changes in environmental conditions (e.g., Rogers-Bennet & Catton 2019) and their nearshore, coastal location directly exposes them to human activities, such as pollution, harvesting, and fishing (Bennett et al. 2016).

Due to its foundational role, changes or impacts to kelp can reverberate throughout the ecosystem and negatively affect many other species. As mentioned previously, kelp is naturally highly variable, and like many other ecological processes, undergoes boom and bust cycles. For over four decades, dense, productive kelp forests have been shown to transition to sea urchin barrens, and back again, in natural cycles (Sala et al. 1998; Pinnegar et al. 2000; Steneck et al. 2002; Figure 2). These transitions are called phase shifts. In a healthy, balanced kelp forest, sea urchins typically passively feed on detrital plant matter, such as broken off pieces of kelp fronds that fall to the seafloor. A phase shift occurs when the grazing intensity of sea urchins increases, resulting in them actively feeding on kelp stalks and fronds to a point where the kelp in an area can become greatly reduced, creating an urchin barren. Sea urchin grazing intensity can change for a number of reasons, including reduction in sea urchin predators (e.g., sea otters, sunflower sea stars) or poor kelp recruitment events (e.g., due to warm water temperature). Regardless of the reason, the phases tend to transition back and forth over time. However, there is concern that sea urchin barrens may become an alternative stable state of the subtidal ecosystem from which kelp in an area cannot recover (Filbee-Dexter & Scheibling 2014). 

Figure 2. Screenshots from GoPro videos from 2016 (left) and 2018 (right) at the same kayak sampling station in Port Orford showing the difference between a dense kelp forest and what appears to be an urchin barren. (Source: GEMM Lab).

For example, in 2014, bull kelp canopy cover in northern California was reduced by >90% and has not shown signs of recovery since (Rogers-Bennet & Catton 2019; Figure 3). This massive decline was attributed to two major events: 1) the onset of sea star wasting disease (SSWD) in 2013 and 2) the “warm blob” of 2014-2016. SSWD affected over 20 sea star species along the coast from Mexico to Alaska, with the predatory sunflower sea star, which consumes purple sea urchins, most affected, including population declines of 80-100% along the coast (Harvell et al. 2019). Following this SSWD outbreak, the “warm blob”, which was an extreme marine heatwave in the Pacific Ocean, caused ocean temperatures to spike. These two events allowed purple sea urchin populations to grow unchecked by their predators, and created nutrient-poor and warm water conditions, which limited kelp growth and productivity. Intense grazing on bull kelp by growing urchin populations resulted in the >90% reduction in bull kelp canopy cover and has left behind widespread urchin barrens instead (Rogers-Bennet & Catton 2019). Consequently, there have been ecological and economic impacts on the ecosystem and communities in northern California. Without bull kelp, red abalone and red sea urchin populations starved, leading to a subsequent loss of the recreational red abalone (estimated value of $44 million/year) and commercial red urchin fisheries in northern California (Rogers-Bennet & Catton 2019).

Figure 3. Surface kelp canopy area pre- and post-impact from sites in Sonoma and Mendocino counties, northern California from aerial surveys (2008, 2014-2016). Figure and figure caption taken from Rogers-Bennett & Catton (2019).

As I mentioned earlier, while phase shifts between kelp forests and urchin barrens are common cycles, the intensity of the events described above in northern California are an example of sea urchin barrens potentially becoming a stable state of the subtidal ecosystem (Filbee-Dexter & Scheibling 2014). Given that marine heatwaves are only expected to increase in intensity and frequency in the future (Frölicher et al. 2018), the events documented in northern California may not be an isolated incidence. 

Considering that parts of the Oregon coast, particularly the southern portion, are very similar to northern California biogeographically, and that it was not exempt from the “warm blob”, similar changes in kelp forests may be occurring along our coast. There are many individuals and groups that are actively working on this issue to examine potential impacts to kelp and the species that depend on the services it provides. For more information, check out the Oregon Kelp Alliance

Figure 4. A gray whale surfaces in a large kelp bed during a foraging bout along the Oregon coast. (Source: GEMM Lab).

So, what does all of this information have to do with gray whales? Given their affinity for kelp habitats (Figure 4) and their zooplankton prey that aggregates there, changes to kelp ecosystems may affect gray whale health and ecology. This aspect of the complex kelp trophic web has not been examined to date; thus one of my PhD chapters focuses on the response of gray whales to changing kelp ecosystems along the southern Oregon coast. To do this, I am examining 6 years of data collected during the TOPAZ/JASPER project in Port Orford, to look at the relationships between kelp health, sea urchin density, zooplankton abundance, and gray whale foraging effort over space and time. Documenting impacts of changing kelp forests on gray whales is important to assist management efforts as healthy and abundant kelp seems critical in providing ample food opportunities for these iconic Pacific Northwest marine predators.

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References

Bennett S, et al. The ‘Great Southern Reef’: Social, ecological and economic value of Australia’s neglected kelp forests. Marine and Freshwater Research 67:47-56.

Dayton PK (1985) Ecology of kelp communities. Annual Review of Ecology and Systematics 16:215-245.

Filbee-Dexter K, Scheibling RE (2014) Sea uechin barrens as alternative stable states of collapsed kelp ecosystems. Marine Ecology Progress Series 495:1-25.

Frölicher TL, Fischer EM, Gruber N (2018) Marine heatwaves under global warming. Nature 560:360-364.

Harvell CD, et al. (2019) Disease epidemic and a marine heat wave are associated with the continental-scale collapse of a pivotal predator (Pycnopodia helianthoides). Science Advances 5(1) doi:10.1126/sciadv.aau7042.

Krumhansl KA, et al. (2016) Global patterns of kelp forest change over the past half-century. Proceedings of the National Academy of Sciences of the United States of America 113(48):13785-13790.

Pinnegar JK, et al. (2000) Trophic cascades in benthic marine ecosystems: lessons for fisheries and protected-area management. Environmental Conservation 27:179-200.

Rogers-Bennett L, Catton CA (2019) Marine heat wave and multiple stressors tip bull kelp forest to sea urchin barrens. Scientific Reports 9:15050.

Sala E, Boudouresque CF, Harmelin-Vivien M (1998) Fishing, trophic cascades and the structure of algal assemblages; evaluation of an old but untested paradigm. Oikos 82:425-439.

Steneck RS, et al. (2002) Kelp forest ecosystems: biodiversity, stability, resilience and future. Environmental Conservation 29:436-459.

Tegner MJ, Dayton PK, Edwards PB, Riser KL (1996) Is there evidence for the long-term climatic change in southern California kelp forests? California Cooperative Oceanic Fisheries Investigations Report 37:111-126.

Torres LG, Nieukirk SL, Lemos L, Chandler TE (2018) Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science doi:10.3389/fmars.2019.00319.

What drives individual specialization?

Clara Bird, PhD Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

When I wrote my first blog on individual specialization well over a year ago, I just skimmed the surface of the literature on this topic and only started to recognize the importance of studying individual specialization. The question, “is there individual specialization in the PCFG of gray whales?” is the focus of my first thesis chapter and the results will affect all my subsequent work. Therefore, the literature and concepts of individual specialization are a focus of my literature review and studies.

In my previous blog I focused on common characteristics of individuals that are similarly specialized as an underlying driver of individual specialization. While these characteristics (often attributable to age, sex, or physical traits) are important to consider, I’ve learned that the list of drivers of individual specialization is long and that many variables are dynamic. Of all the drivers I’ve learned about, competition is among the most common.

Competition is a major driver of individual specialization, and a common driver of competition is resource availability. When resource availability decreases, whether caused by increasing population density or changing environmental conditions, competition for that resource increases. As competition increases, individuals have a choice. They can choose to engage in competition, either by racing, fighting, or sharing [1], which can be costly, or they can diffuse the competition by focusing on a different resource.  This second approach would be considered niche partitioning in the prey dimension. Niche partitioning is a way for individuals to share ecological space by using different resources. Essentially, individuals can share habitat without having to engage in direct competition by pursuing different prey types [2]. 

This switch to different prey types can change the degree of individual specialization present in the population (Figure 1). But the direction of the change is not constant. If all individuals were pursuing the same prey type under low competition conditions but then switched to different alternate prey types under high competition, then individual specialization would increase (Figure 1a). This direction has been observed across a range of species including sharks [3], otters [4]–[7], dolphins [8], [9], stickleback fish [10], [11], largemouth bass [12], banded mongoose [13], fur seals [14], and baleen whales [15].

However, if individuals were pursuing different prey types under low competition conditions (maybe because of underlying differences such as age or sex) but then switched to the same alternate prey types under high competition, diet overlap would increase, and individual specialization would decrease (Figure 1b). Furthermore, an individual might not switch to an entirely new prey type but instead add prey items to its diet [16]. This diet expansion under competition would also decrease individual specialization. Fewer studies have reported this direction but it’s been found in the common bumblebee [17] and in several neotropical vertebrate species [18], [19].

Figure `1. Figure 3 from Araújo et al. 2011 [20]. Illustration of how ecological mechanisms may affect the degree of individual specialization. Arrows linking resources to individual consumers indicate resource consumption (relative thickness indicates proportional contribution). 
Horizontal arrows indicate the sign (positive or negative) of the effect on the degree of individual specialization. (a) Consumers share the same preferred resource (dark gray tangle) but have different alternative resources (white and light gray triangles). As the preferred resource becomes scarce, consumers switch to different alternatives, increasing the degree of individual specialization. (b) Alternatively, consumers have distinct preferred resources, so that as resources become scarce, individuals converge to the alternative resource (dark gray triangle), reducing diet variation.

Interestingly, its hypothesized that individual specialization driven by competition is one of the factors that facilitates the formation and existence of stable groups [21]. For example, a study on resident female dolphins in Sarasota Bay, FL, USA found that females with high spatial overlap used distinct foraging specializations [8](Figure 2). This study illustrates how partitioning prey enabled spatial and social coexistence. A study on banded mongooses reached a similar conclusion [13]. They found that specialization was highest in the biggest groups (with the most competition) and not explained by sex, age, or other inherent differences. They hypothesized that specialization increasing with competition reduced conflict and allowed the groups to remain stable. This study also highlighted the role of learning to determine an individual’s specialization.

Figure 2. A bottlenose dolphin.
Source: https://sarasotadolphin.org

Learning drives the distribution of knowledge throughout a population, which can lead to either specialization or generalization. ‘One-to-one’ learning, where one individual learns from one demonstrator, tends to promote individual specialization [21]. This form of transmission drives specialization because the individuals who learn the specialization tend to then carry on using, and eventually teaching, that specialization [6]. A common example of ‘one-to-one’ learning is vertical transmission from parent to offspring. It has been shown to transmit specializations in dolphins [22] and otters [6]. ‘One-to-one’ learning can occur outside of parent-offspring pairs; non-parent-offspring ‘one-to-one’ learning has been shown to drive specialization in banded mongooses [13](Figure 3).

However, other forms of social learning can promote more generalized foraging strategies. ‘Many-to-one’ or ‘one-to-many’ learning  can reduce the presence of specialization in species [13], [21] as can the presence of conformity in a group [23], [24].

Figure 3. A group of banded mongooses.
Source: http://socialisresearch.org/about-the-banded-mongoose-project/

The multiple drivers of specialization and their dynamic quality means that it is important to contextualize specialization. For example, a study on four species of neotropical frogs found varying degrees of specialization across multiple populations of each species [18]. The degree of specialization was dependent on a variety of drivers including predation and both intra- and inter-specific competition. Notably, the direction of the relationship between degree of specialization and each driver was species specific. This study highlights that one species may not always be more specialized than another, but that a populations’ specialization is context dependent.

Therefore, it is important to not only be aware of the degree of specialization present in a population, but to also understand its dynamics and drivers. These relationships can then be used to understand how, and why, a population may react to competition from other species, predators, and changes in resource availability [20].  A population’s specialization can also affect the specialization of other populations and community dynamics [25], therefore, it’s important to consider and study individual specialization on both the population and community level. I am excited to start using our incredible six-year dataset to start investigating these questions for PCFG gray whales, so stay tuned for results!

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References

[1]       M. Taborsky, M. A. Cant, and J. Komdeur, The Evolution of Social Behaviour. Cambridge: Cambridge University Press, 2021. doi: 10.1017/9780511894794.

[2]       E. R. Pianka, “Niche Overlap and Diffuse Competition,” vol. 71, no. 5, pp. 2141–2145, 1974.

[3]       P. Matich et al., “Ecological niche partitioning within a large predator guild in a nutrient-limited estuary,” Limnol. Oceanogr., vol. 62, no. 3, pp. 934–953, 2017, doi: https://doi.org/10.1002/lno.10477.

[4]       S. D. Newsome et al., “The interaction of intraspecific competition and habitat on individual diet specialization: a near range-wide examination of sea otters,” Oecologia, vol. 178, no. 1, pp. 45–59, May 2015, doi: 10.1007/s00442-015-3223-8.

[5]       M. T. Tinker, G. Bentall, and J. A. Estes, “Food limitation leads to behavioral diversification and dietary specialization in sea otters,” Proc. Natl. Acad. Sci., vol. 105, no. 2, pp. 560–565, Jan. 2008, doi: 10.1073/pnas.0709263105.

[6]       M. T. Tinker, M. Mangel, and J. A. Estes, “Learning to be different: acquired skills, social learning, frequency dependence, and environmental variation can cause behaviourally mediated foraging specializations,” Evol. Ecol. Res., vol. 11, pp. 841–869, 2009.

[7]       M. T. Tinker et al., “Structure and mechanism of diet specialisation: testing models of individual variation in resource use with sea otters,” Ecol. Lett., vol. 15, no. 5, pp. 475–483, 2012, doi: 10.1111/j.1461-0248.2012.01760.x.

[8]       S. Rossman et al., “Foraging habits in a generalist predator: Sex and age influence habitat selection and resource use among bottlenose dolphins (Tursiops truncatus),” Mar. Mammal Sci., vol. 31, no. 1, pp. 155–168, 2015, doi: https://doi.org/10.1111/mms.12143.

[9]       L. G. Torres, “A kaleidoscope of mammal , bird and fish : habitat use patterns of top predators and their prey in Florida Bay,” vol. 375, pp. 289–304, 2009, doi: 10.3354/meps07743.

[10]     M. S. Araújo et al., “Network Analysis Reveals Contrasting Effects of Intraspecific Competition on Individual Vs. Population Diets,” Ecology, vol. 89, no. 7, pp. 1981–1993, 2008, doi: 10.1890/07-0630.1.

[11]     R. Svanbäck and D. I. Bolnick, “Intraspecific competition drives increased resource use diversity within a natural population,” Proc. R. Soc. B Biol. Sci., vol. 274, no. 1611, pp. 839–844, Mar. 2007, doi: 10.1098/rspb.2006.0198.

[12]     D. E. Schindler, J. R. Hodgson, and J. F. Kitchell, “Density-dependent changes in individual foraging specialization of largemouth bass,” Oecologia, vol. 110, no. 4, pp. 592–600, May 1997, doi: 10.1007/s004420050200.

[13]     C. E. Sheppard et al., “Intragroup competition predicts individual foraging specialisation in a group-living mammal,” Ecol. Lett., vol. 21, no. 5, pp. 665–673, 2018, doi: 10.1111/ele.12933.

[14]     L. Kernaléguen, J. P. Y. Arnould, C. Guinet, and Y. Cherel, “Determinants of individual foraging specialization in large marine vertebrates, the Antarctic and subantarctic fur seals,” J. Anim. Ecol., vol. 84, no. 4, pp. 1081–1091, 2015, doi: 10.1111/1365-2656.12347.

[15]     E. M. Keen and K. M. Qualls, “Respiratory behaviors in sympatric rorqual whales: the influence of prey depth and implications for temporal access to prey,” J. Mammal., vol. 99, no. 1, pp. 27–40, Feb. 2018, doi: 10.1093/jmammal/gyx170.

[16]     R. H. MacArthur and E. R. Pianka, “On Optimal Use of a Patchy Environment,” Am. Nat., vol. 100, no. 916, pp. 603–609, 1966, doi: 10.1086/282454.

[17]     C. Fontaine, C. L. Collin, and I. Dajoz, “Generalist foraging of pollinators: diet expansion at high density,” J. Ecol., vol. 96, no. 5, pp. 1002–1010, 2008, doi: 10.1111/j.1365-2745.2008.01405.x.

[18]     R. Costa-Pereira, V. H. W. Rudolf, F. L. Souza, and M. S. Araújo, “Drivers of individual niche variation in coexisting species,” J. Anim. Ecol., vol. 87, no. 5, pp. 1452–1464, 2018, doi: 10.1111/1365-2656.12879.

[19]     M. M. Pires, P. R. Guimarães Jr, M. S. Araújo, A. A. Giaretta, J. C. L. Costa, and S. F. dos Reis, “The nested assembly of individual-resource networks,” J. Anim. Ecol., vol. 80, no. 4, pp. 896–903, 2011, doi: 10.1111/j.1365-2656.2011.01818.x.

[20]     M. S. Araújo, D. I. Bolnick, and C. A. Layman, “The ecological causes of individual specialisation,”Ecol. Lett., vol. 14, no. 9, pp. 948–958, 2011, doi: https://doi.org/10.1111/j.1461-0248.2011.01662.x.

[21]     C. E. Sheppard, R. Heaphy, M. A. Cant, and H. H. Marshall, “Individual foraging specialization in group-living species,” Anim. Behav., vol. 182, pp. 285–294, Dec. 2021, doi: 10.1016/j.anbehav.2021.10.011.

[22]     S. Wild, S. J. Allen, M. Krützen, S. L. King, L. Gerber, and W. J. E. Hoppitt, “Multi-network-based diffusion analysis reveals vertical cultural transmission of sponge tool use within dolphin matrilines,” Biol. Lett., vol. 15, no. 7, p. 20190227, Jul. 2019, doi: 10.1098/rsbl.2019.0227.

[23]     L. M. Aplin, D. R. Farine, J. Morand-Ferron, A. Cockburn, A. Thornton, and B. C. Sheldon, “Experimentally induced innovations lead to persistent culture via conformity in wild birds,” Nature, vol. 518, no. 7540, pp. 538–541, Feb. 2015, doi: 10.1038/nature13998.

[24]     E. Van de Waal, C. Borgeaud, and A. Whiten, “Potent Social Learning and Conformity Shape a Wild Primate’s Foraging Decisions,” Science, Apr. 2013, doi: 10.1126/science.1232769.

[25]     D. I. Bolnick et al., “Why intraspecific trait variation matters in community ecology,” Trends Ecol. Evol., vol. 26, no. 4, pp. 183–192, Apr. 2011, doi: 10.1016/j.tree.2011.01.009.

Marine megafauna as ecosystem sentinels: What animals can tell us about changing oceans

By Dawn Barlow1 and Will Kennerley2

1PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

2MS Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Seabird Oceanography Lab

The marine environment is dynamic, and mobile animals must respond to the patchy and ephemeral availability of resource in order to make a living (Hyrenbach et al. 2000). Climate change is making ocean ecosystems increasingly unstable, yet these novel conditions can be difficult to document given the vast depth and remoteness of most ocean locations. Marine megafauna species such as marine mammals and seabirds integrate ecological processes that are often difficult to observe directly, by shifting patterns in their distribution, behavior, physiology, and life history in response to changes in their environment (Croll et al. 1998, Hazen et al. 2019). These mobile marine animals now face additional challenges as rising temperatures due to global climate change impact marine ecosystems worldwide (Hazen et al. 2013, Sydeman et al. 2015, Silber et al. 2017, Becker et al. 2019). Given their mobility, visibility, and integration of ocean processes across spatial and temporal scales, these marine predator species have earned the reputation as effective ecosystem sentinels. As sentinels, they have the capacity to shed light on ecosystem function, identify risks to human health, and even predict future changes (Hazen et al. 2019). So, let’s explore a few examples of how studying marine megafauna has revealed important new insights, pointing toward the importance of monitoring these sentinels in a rapidly changing ocean.

Cairns (1988) is often credited as first promoting seabirds as ecosystem sentinels and noted several key reasons why they were perfect for this role: (1) Seabirds are abundant, wide-ranging, and conspicuous, (2) although they feed at sea, they must return to land to nest, allowing easier observation and quantification of demographic responses, often at a fraction of the cost of traditional, ship-based oceanographic surveys, and therefore (3) parameters such as seabird reproductive success or activity budgets may respond to changing environmental conditions and provide researchers with metrics by which to assess the current state of that ecosystem.

The unprecedented 2014-2016 North Pacific marine heatwave (“the Blob”) caused extreme ecosystem disruption over an immense swath of the ocean (Cavole et al. 2016). Seabirds offered an effective and morbid indication of the scale of this disruption: Common murres (Uria aalge), an abundant and widespread fish-eating seabird, experienced widespread breeding failure across the North Pacific. Poor reproductive performance suggested that there may have been fewer small forage fish around and that these changes occurred at a large geographic scale. The Blob reached such an extreme as to kill immense numbers of adult birds, which professional and community scientists found washed up on beach-surveys; researchers estimate that an incredible 1,200,000 murres may have died from starvation during this period (Piatt et al. 2020). While the average person along the Northeast Pacific Coast during this time likely didn’t notice any dramatic difference in the ocean, seabirds were shouting at us that something was terribly wrong.

Happily, living seabirds also act as superb ecosystem sentinels. Long-term research in the Gulf of Maine by U.S. and Canadian scientists monitors the prey species provisioned by adult seabirds to their chicks. Will has spent countless hours over five summers helping to conduct this research by watching terns (Sterna spp.) and Atlantic puffins (Fratercula arctica) bring food to their young on small islands off the Maine coast. After doing this work for multiple years, it’s easy to notice that what adults feed their chicks varies from year to year. It was soon realized that these data could offer insight into oceanographic conditions and could even help managers assess the size of regional fish stocks. One of the dominant prey species in this region is Atlantic herring (Clupea harengus), which also happens to be the focus of an economically important fishery.  While the fishery targets four or five-year-old adult herring, the seabirds target smaller, younger herring. By looking at the relative amounts and sizes of young herring collected by these seabirds in the Gulf of Maine, these data can help predict herring recruitment and the relative number of adult herring that may be available to fishers several years in the future (Scopel et al. 2018).  With some continued modelling, the work that we do on a seabird colony in Maine with just a pair of binoculars can support or maybe even replace at least some of the expensive ship-based trawl surveys that are now a popular means of assessing fish stocks.

A common tern (Sterna hirundo) with a young Atlantic herring from the Gulf of Maine, ready to feed its chick (Photo courtesy of the National Audubon Society’s Seabird Institute)

For more far-ranging and inaccessible marine predators such as whales, measuring things such as dietary shifts can be more challenging than it is for seabirds. Nevertheless, whales are valuable ecosystem sentinels as well. Changes in the distribution and migration phenology of specialist foragers such as blue whales (Balaenoptera musculus) and North Atlantic right whales (Eubalaena glacialis) can indicate relative changes in the distribution and abundance of their zooplankton prey and underlying ocean conditions (Hazen et al. 2019). In the case of the critically endangered North Atlantic right whale, their recent declines in reproductive success reflect a broader regime shift in climate and ocean conditions. Reduced copepod prey has resulted in fewer foraging opportunities and changing foraging grounds, which may be insufficient for whales to obtain necessary energetic stores to support calving (Gavrilchuk et al. 2021, Meyer-Gutbrod et al. 2021). These whales assimilate and showcase the broad-scale impacts of climate change on the ecosystem they inhabit.

Blue whales that feed in the rich upwelling system off the coast of California rely on the availability of their krill prey to support the population (Croll et al. 2005). A recent study used acoustic monitoring of blue whale song to examine the timing of annual population-level transition from foraging to breeding migration compared to oceanographic variation, and found that flexibility in timing may be a key adaptation to persistence of this endangered population facing pressures of rapid environmental change (Oestreich et al. 2022). Specifically, blue whales delayed the transition from foraging to breeding migration in years of the highest and most persistent biological productivity from upwelling, and therefore listening to the vocalizations of these whales may be valuable indicator of the state of productivity in the ecosystem.

Figure reproduced from Oestreich et al. 2022, showing relationships between blue whale life-history transition and oceanographic phenology of foraging habitat. Timing of the behavioral transition from foraging to migration (day of year on the y-axis) is compared to (a) the date of upwelling onset; (b) the date of peak upwelling; and (c) total upwelling accumulated from the spring transition to the end of the upwelling season.

In a similar vein, research by the GEMM Lab on blue whale ecology in New Zealand has linked their vocalizations known as D calls to upwelling conditions, demonstrating that these calls likely reflect blue whale foraging opportunities (Barlow et al. 2021). In ongoing analyses, we are finding that these foraging-related calls were drastically reduced during marine heatwave conditions, which we know altered blue whale distribution in the region (Barlow et al. 2020). Now, for the final component of Dawn’s PhD, she is linking year-round environmental conditions to the occurrence patterns of different blue whale vocalization types, hoping to shed light on ecosystem processes by listening to the signals of these ecosystem sentinels.

A blue whale comes up for air in the South Taranaki Bight of New Zealand. photo by L. Torres.

It is important to understand the widespread implications of the rapidly warming climate and changing ocean conditions on valuable and vulnerable marine ecosystems. The cases explored here in this blog exemplify the importance of monitoring these marine megafauna sentinel species, both now and into the future, as they reflect the health of the ecosystems they inhabit.

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References:

Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG (2020) Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar Ecol Prog Ser 642:207–225.

Barlow DR, Klinck H, Ponirakis D, Garvey C, Torres LG (2021) Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep 11:1–10.

Becker EA, Forney KA, Redfern J V., Barlow J, Jacox MG, Roberts JJ, Palacios DM (2019) Predicting cetacean abundance and distribution in a changing climate. Divers Distrib 25:626–643.

Cairns DK (1988) Seabirds as indicators of marine food supplies. Biol Oceanogr 5:261–271.

Cavole LM, Demko AM, Diner RE, Giddings A, Koester I, Pagniello CMLS, Paulsen ML, Ramirez-Valdez A, Schwenck SM, Yen NK, Zill ME, Franks PJS (2016) Biological impacts of the 2013–2015 warm-water anomaly in the northeast Pacific: Winners, losers, and the future. Oceanography 29:273–285.

Croll DA, Marinovic B, Benson S, Chavez FP, Black N, Ternullo R, Tershy BR (2005) From wind to whales: Trophic links in a coastal upwelling system. Mar Ecol Prog Ser 289:117–130.

Croll DA, Tershy BR, Hewitt RP, Demer DA, Fiedler PC, Smith SE, Armstrong W, Popp JM, Kiekhefer T, Lopez VR, Urban J, Gendron D (1998) An integrated approch to the foraging ecology of marine birds and mammals. Deep Res Part II Top Stud Oceanogr.

Gavrilchuk K, Lesage V, Fortune SME, Trites AW, Plourde S (2021) Foraging habitat of North Atlantic right whales has declined in the Gulf of St. Lawrence, Canada, and may be insufficient for successful reproduction. Endanger Species Res 44:113–136.

Hazen EL, Abrahms B, Brodie S, Carroll G, Jacox MG, Savoca MS, Scales KL, Sydeman WJ, Bograd SJ (2019) Marine top predators as climate and ecosystem sentinels. Front Ecol Environ 17:565–574.

Hazen EL, Jorgensen S, Rykaczewski RR, Bograd SJ, Foley DG, Jonsen ID, Shaffer SA, Dunne JP, Costa DP, Crowder LB, Block BA (2013) Predicted habitat shifts of Pacific top predators in a changing climate. Nat Clim Chang 3:234–238.

Hyrenbach KD, Forney KA, Dayton PK (2000) Marine protected areas and ocean basin management. Aquat Conserv Mar Freshw Ecosyst 10:437–458.

Meyer-Gutbrod EL, Greene CH, Davies KTA, Johns DG (2021) Ocean regime shift is driving collapse of the north atlantic right whale population. Oceanography 34:22–31.

Oestreich WK, Abrahms B, Mckenna MF, Goldbogen JA, Crowder LB, Ryan JP (2022) Acoustic signature reveals blue whales tune life history transitions to oceanographic conditions. Funct Ecol.

Piatt JF, Parrish JK, Renner HM, Schoen SK, Jones TT, Arimitsu ML, Kuletz KJ, Bodenstein B, Garcia-Reyes M, Duerr RS, Corcoran RM, Kaler RSA, McChesney J, Golightly RT, Coletti HA, Suryan RM, Burgess HK, Lindsey J, Lindquist K, Warzybok PM, Jahncke J, Roletto J, Sydeman WJ (2020) Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016. PLoS One 15:e0226087.

Scopel LC, Diamond AW, Kress SW, Hards AR, Shannon P (2018) Seabird diets as bioindicators of atlantic herring recruitment and stock size: A new tool for ecosystem-based fisheries management. Can J Fish Aquat Sci.

Silber GK, Lettrich MD, Thomas PO, Baker JD, Baumgartner M, Becker EA, Boveng P, Dick DM, Fiechter J, Forcada J, Forney KA, Griffis RB, Hare JA, Hobday AJ, Howell D, Laidre KL, Mantua N, Quakenbush L, Santora JA, Stafford KM, Spencer P, Stock C, Sydeman W, Van Houtan K, Waples RS (2017) Projecting marine mammal distribution in a changing climate. Front Mar Sci 4:413.

Sydeman WJ, Poloczanska E, Reed TE, Thompson SA (2015) Climate change and marine vertebrates. Science 350:772–777.